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Record W2734421180 · doi:10.1097/jom.0000000000001107

Disseminating Pesticide Exposure Results to Farmworker and Nonfarmworker Families in an Agricultural Community

2017· article· en· W2734421180 on OpenAlexaff
Beti Thompson, Elizabeth Carosso, William C. Griffith, Tomomi Workman, Sarah D. Hohl, Elaine M. Faustman

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsDisseminationInformation DisseminationEnvironmental healthCitizen journalismCommunity-based participatory researchAgricultureParticipatory action researchPsychologyMedicineGeographyComputer scienceWorld Wide WebSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to examine the impact of a dissemination process to provide individual pesticide results to study participants. METHODS: After working with community members to disseminate data, 37 participants were recontacted via an interview survey to assess the effectiveness of the dissemination process. RESULTS: Almost all participants (97.3%) recalled a home visit from a health promoter; 29 (78.4%) correctly recalled that the health promoter used a thermometer or graphic to explain the results; 26 (70.3%) correctly interpreted graphics showing high and low exposure levels in adults and 75.7% correctly interpreted results for children. CONCLUSIONS: The study results support the use of a community-based participatory research approach to decide how to best depict and disseminate study results, especially among participants who are often left out of the dissemination process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.289
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2017
Admission routes1
Has abstractyes

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Same venueJournal of Occupational and Environmental MedicineSame topicPesticide Exposure and ToxicityFrench-language works237,207